Browsing by Author "Basaran, K"

Now showing 1 - 10 of 10
  • No Thumbnail Available
    Item
    Energy management for on-grid and off-grid wind/PV and battery hybrid systems
    Basaran, K; Cetin, NS; Borekci, S
    Renewable energy systems such as photovoltaic (PV) and wind energy systems are widely designed grid connected or autonomous. This is a problem especially in small powerful system due to the restriction on the inverter markets. Inverters which are utilised in these kinds of energy systems operate on grid or off grid. In this study, a novel power management strategy has been developed by designing a wind-PV hybrid system to operate both as an autonomous system and as a grid-connected system. The inverter used in this study has been designed to operate both on-grid and off-grid. Due to the continuous demand for energy, gel batteries are used in the hybrid system. The designed Power Management Unit performs measurement from various points in the system and in accordance with this measurement; it provides an effective energy transfer to batteries, loads and grid. The designed control unit provided the opportunity to work more efficiently up to 10% rate.
  • No Thumbnail Available
    Item
    Performance Analysis of a PV/T Based System by Using MATLAB/Simulink
    Koç, I; Basaran, K
    In this study, electrical and thermal efficiency analyses of photovoltaic/thermal (PV/T) collectors were performed. For this purpose, a liquid type flat PV/T collector system which is suitable for hot water supply and electrical power production is used. Various parameters such as sealing factor, radiation, inlet temperature, ambient temperature, absorbent plate parameters (tube spacing, pipe diameter, flap thickness, etc.) and the thermal conductivity of the fluid in the absorbent plate must be taken into consideration for such systems to operate with maximum efficiency. The thermal efficiency of the PV/T collectors is significantly influenced by the ratio of the temperature difference between the inlet temperature and the ambient temperature (Ti-Ta) to the global solar radiation (G) falling on the collector surface. (Ti-Ta) / G ratio increase decreases the thermal efficiency. In addition, other factors affecting thermal efficiency are the sealing factor (s) and the different flap ratio (d/w) values. The increase of the d/w ratio also increases the collector area and decreases the PV module temperature, thus increasing the electrical efficiency. The major problem in PV/T systems is that optimum efficiency can be achieved by considering all these parameters. In this study, a MATLAB/Simulink model of PV/T collector was prepared by using mathematical equations. With the help of this model, the design parameters and their effects to thermal and electrical efficiency were investigated. The maximum thermal efficiency, electrical efficiency and total efficiency of the PV/T collector were determined as 64.5%, 13.5% and 78%, respectively.
  • No Thumbnail Available
    Item
    Error sources and measurement uncertainties in outdoor testing of BIPV modules
    Basaran, K
    Although building-integrated photovoltaic (BIPV) systems have great potential, investment in this field is not at the desired level. There are two main reasons for this: the lack of technical analysis and economic reasons. Manufacturers and investors utilize datasheets of modules to determine the systems performance, which is determined at standard test conditions (STCs). However, there are apparent differences between STCs and outdoor measurements. Most studies in the field of BIPV system analysis have only focused on long-term outdoor measurements. Besides that, uncertainty of measurements is necessary to achieve scientific results. The aim of this study is to emphasize the importance of measurement uncertainty, to describe how measurement uncertainties are calculated, and to find out the uncertainty of an outdoor BIPV measuring system. In this study, different roof-integrated photovoltaic systems with 15, 30, and 45 inclination angles were tested in the Fraunhofer Institute for Wind Energy and Energy System Technology measurement field. Maximum power point current, voltage, power, and temperature of each system were measured. The uncertainty of current, voltage, and temperature was calculated as 0.29%, 0.05%, and 1.15%, respectively.
  • No Thumbnail Available
    Item
    Optimal expansion planning of electrical energy distribution substation considering hydrogen storage
    Basaran, K; Öztürk, H
    Electricity network operators undertake important responsibilities such as balancing electricity demand and supply, minimizing power outages, and making the necessary maintenance, repairs, and investments to provide safe and continuous energy. In this context, the most significant challenge encountered today is the need for a more immediate renewal of distribution grid expansion plans, owing to the rapid increase in energy demand and the grid integration of renewable energy power plants and electric vehicle charging stations. In order to find a solution to this issue, an active distribution network has been analyzed under five scenarios based on load demand forecasting. The artificial neural network method has been employed for the forecast of load demand, and the DigSilent Power Factory (DPF) model of the distribution network has been utilized to analyze the effects of scenarios. Connecting PV plants with capacities of 3 MW and 5 MW to different feeders in the distribution network, along with Hydrogen Energy Storage (HES) with a capacity of 1 MW to one feeder, has resulted in a reduction of the distribution transformer's occupancy rate from 79.8% to 70.6%. The contribution of a 1 MW HES system to the transformer occupancy rate was determined to be 1.3%. The results highlight the importance of considering the annual load demand forecast, as well as the network integration of PV power plants, electric vehicle charging stations, and hydrogen storage in grid expansion planning.
  • No Thumbnail Available
    Item
    A New Approach for Prediction of Solar Radiation with Using Ensemble Learning Algorithm
    Basaran, K; Özçift, A; Kilinç, D
    This article investigates the competence of ensemble learning techniques in solar irradiance prediction. It was seen from the literature survey, an ensemble tree model, random forests is studied more frequently as ensemble models. However, ensemble of support vector regression (SVR) and artificial neural networks (ANN) is also possible. So, this study is the first detailed evaluation of ensemble models in solar irradiance estimation domain. Boosting and bagging ensembles of SVR, ANN and decision tree (DT), are developed to estimate solar irradiance in hourly basis in five cities in Turkey. First frequently used base models (SVR, ANN, and DT) are created and tested with the use of 5 years meteorological data. Then boosting and bagging ensembles of the base models are developed and tested with the same data. The base models are compared with their ensemble counterparts in terms of average coefficient of determination (R-2) and root mean squared error (RMSE). The comparative results show that boosting and bagging ensemble models improve SVR, ANN, and DT in terms of RMSE between 4.6 and 14.6% in average. The results show empirically that ensemble models improve prediction accuracies of various base regression models and it can be applied to other machine learning models used in solar irradiance prediction.
  • No Thumbnail Available
    Item
    The effect of behavior changes caused by the covid-19 pandemic on electricity consumptions and feeder loads: a case study on an electricity distribution feeder
    Öztürk, H; Basaran, K
    The COVID-19 (Sars CoV-2) virus, which emerged in Wuhan city of Hubei province of China in December 2019, affected the whole world in a short time and was declared a global epidemic by the World Health Organization (WHO) as of March 11, 2020. After this date, closure measures have been implemented all over the world to prevent the spread of the virus. Due to the provisions taken, there have been changes in electrical energy consumption compared to previous years. In March, April and May 2020, when the restrictions affected human life the most, dynamic changes occurred in energy demand all over the world. This has affected international energy markets, energy production and grid load planning. Although the total electricity consumption in Turkey increased compared to the previous year, there was a decrease in the consumption in the commercial tariff. In this study, the effects of the COVID-19 pandemic on electricity consumption were analyzed by analyzing the electricity consumption of Turkey and Izmir, depending on the tariffs, based on time. A case study was conducted on an electricity distribution feeder to see the impact of COVID-19 on electricity distribution networks. For the case study, an electricity distribution feeder with 99% of the subscriber density in the residential and commercial tariff group was selected. For the feeder, load forecasting was made using artificial neural networks machine learning method according to 2018, 2019 and 2020 data. In the load forecasting study, 75% of the data was selected for learning and 25% for testing. As a result of the study, the actual and forecasted load data of 2020 were compared. The effects of the COVID-19 pandemic on the lad of an electricity distribution feeder were investigated. In the study, the best performance values of load forecasting were found mse as 0.0024 and R2 as 0.83.
  • No Thumbnail Available
    Item
    Effect of Irradiance Measurement Sensors on the Performance Ratio of Photovoltaic Power Plant Under Real Operating Conditions: An Experimental Assessment in Turkey
    Basaran, K
    The performance analysis of photovoltaic (PV) plants plays an important role in making investment decisions, creating more efficient designs, and in the operation and maintenance decisions in new plants. In addition, performance analysis provides a way to identify real-time operational problems and allows comparisons among different PV plants. Performance of PV plants can be indexed with many parameters, but service providers are giving energy guarantees according to their performance ratio (PR). There are many factors that affect PR, such as irradiation, temperature, wind speed, humidity and soiling. But, the most important factors affecting PR are irradiation and temperature. Hence, both the irradiation and the temperature must be measured correctly. Solar irradiation is usually measured with a pyranometer or reference cell. However, there are differences between the obtained data from these two sensors, which affects the PR significantly. In this study, the PR of an MW PV plant installed in Turkey was investigated. Two types of irradiation sensors (pyranometer and reference cells) and PVGIS program were used. The data were collected every 5 min during 1 year. The data passed through quality control and filtered process and some improper values were removed from the row data set. In addition, new data were written instead of the missing data. PR was calculated by using conventional and corrected method. As a result, the average annual PR is determined to be 81.05%, 79.04% and 78.96% and the corrected PR is determined to be 83.69%, 82.48% and 80.87% using the reference cell, pyranometer and PVGIS program, respectively.
  • No Thumbnail Available
    Item
    Investigating the Effects of Selecting Different Slack Bus on Power Systems
    Sabati, A; Basaran, K; Bayindir, R; Padmanaban, S; Siano, P; Leonowicz, Z
    The load-flow study is a numerical analysis of the flow of electric power in an interconnected system in power engineering. Simplified notation such as the one-line diagram and per-unit system is used for power-flow study and focuses on various features of AC power parameters, such as voltages, voltage angles, real power and reactive power. It analyses the power systems in normal steady-state operation. So, the load flow studies play a major role in analyzing of the power systems. Generally, power system buses are categorized into three classes named load bus, power grid bus and slack bus or swing bus. In fact, slack buses in the power system are chosen among PV buses and also voltage value and phase angle of slack buses must be set 1 and 0, respectively. In this study, analysis of power flow was performed on six bus bar system using Gauss Seidel method. The study analyzed the change of effects that come from choosing different slack buses to determine the critical stress points, voltage stability, in the multi-bus systems. At the beginning of the each power flow analysis, different busses which have generator were determined as the slack bus and the analysis was performed without increasing the load. The effects of changes in critical points were examined at the end of the all possible analysis.
  • No Thumbnail Available
    Item
    Systematic literature review of photovoltaic output power forecasting
    Basaran, K; Bozyigit, F; Siano, P; Taser, PY; Kilinç, D
    Since the harmful effects of climate warming on our planet were first observed, the use of renewable energy resources has been significantly increasing. Among the potential renewable energy sources, photovoltaic (PV) system installations keep continuously increasing world-wide due to its economic and environmental contributions. Despite its significant benefits, the inherent variability of PV power generation due to meteorological parameters can cause power management/planning problems. Thus, forecasting of PV output data (directly or indirectly) in an accurate manner is a critical task to provide stability, reliability, and optimisation of the grid systems. In considering the literature reviewed, there are various research items utilizing PV output power forecasting. In this study, a systematic literature review based on the search of primary studies (published between 2010 and 2020), which forecast PV power generation using machine learning and deep learning methods, is reported. The studies are evaluated based on the PV material used, their approaches, generated outputs, data set used, and the performance evaluation methods. As a result, gaps and improvable points in the existing literature are revealed, and suggestions which include novelties are offered for future works.
  • No Thumbnail Available
    Item
    A short-term photovoltaic output power forecasting based on ensemble algorithms using hyperparameter optimization
    Basaran, K; Çelikten, A; Bulut, H
    The stochastic and intermittent nature of solar energy presents the power grid with the challenge of providing a stable, secure, and economical power supply, especially in the case of large-scale penetration. The prerequisite for addressing these challenges is accurate power output estimation from PV systems. In addition, accurate power estimation also ensures the correct sizing of PV systems for investors. In this study, the PV output prediction model has been developed based on ensemble algorithms using two years of real power and meteorological data from grid-connected PV systems. Grid search, random search, and Bayesian optimization were used to determine the optimal hyperparameters for ensemble algorithms. The originality of this study is that (i) the use of hyperparameter optimization for ensemble algorithms in predicting PV performance, (ii) the degradation rate of PV panels by ensemble algorithms using the first two years' data, and (iii) the performance comparison of ensemble algorithms using the hyperparameter optimization technique. The accuracy and precision of the prediction model are determined by the relative root mean square error (RMSE), mean absolute error (MAE), mean bias error (MBE), mean scaled error (MSE), coefficient of determination (R2), mean absolute percentage error (MAPE), and maximum absolute error (MaxAE). To the best of our knowledge, this is one of the first studies to address the optimization of all hyperparameters to find the best parameters for ensemble algorithms and PV panel degradation rates. The results show that the CatBoost algorithm has better performance than the other algorithms used. The performance metrics of the CatBoost algorithm were determined to be 0.9327 R2, 0.047 MSE, 0.0388 MAE, 0.0003 MBE, 0.069 RMSE, 18.7 MAPE, and 0.79 MaxAE.